Can Shareholders Be at Rest after Adopting Clawback Provisions? Evidence from Stock Price Crash Risk
Bibliographic record
Abstract
Abstract Using a propensity score matched sample and a difference‐in‐differences research design, we find that stock price crash risk increases after a firm voluntarily incorporates clawback provisions in executive officers' compensation contracts. This heightened crash risk is concentrated in adopters that increase upward real activities‐based earnings management and those that reduce the readability of 10‐K reports. Based on cross‐sectional analyses, we also find that the increased crash risk is more pronounced for adopters with high ex ante fraud risk, low‐ability managers, high CEO equity incentives, and low dedicated institutional ownership. Collectively, our results suggest that the clawback adoption per se does not curb managerial opportunism but rather induces managers to use alternative channels for concealing bad news, which may contribute to a greater stock price crash risk; and the increase in crash risk is more likely in cases where incentives are strong or monitoring is weak. Our results should be of interest to regulators and policymakers considering the effects of clawback adoption on the investing public.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".